BEST-COST GitHub repository

Let’s start by checking out the BEST-COST GitHub repo and the README file

Landing page of the BEST-COST GitHub repo. The README file tells you how to get started. The folder r_package contains package-related files (including function code). Developments are discussed and documented under Issues.

Landing page of the BEST-COST GitHub repo. The README file tells you how to get started. The folder r_package contains package-related files (including function code). Developments are discussed and documented under Issues.

healthiar in RStudio 1/2

Post installation, you can access the healthiar package landing page in RStudio by going to the Packages tab and then clicking on the healthiar package.

healthiar in RStudio 2/2

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

Example: attribute call without input uncertainties

Attribute COPD cases to air pollution

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_increment = 10, 
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # COPD incidence
  ) 
results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_lower = exdat_pm_copd$relative_risk_lower,
    rr_upper = exdat_pm_copd$relative_risk_upper,
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidents_per_100_000_per_year/1E5*exdat_pm_copd$population_at_risk,
    # bhd_central = exdat_pm_copd$incidence # Uncomment once change committed to main
  )

Output structure

Every attribute output consists of two lists (“folders”)

  • health_main contains the main results

  • health_detailed detailed results (and in some cases even more information about the assessment/calculation)

Different ways to access the results

Tip

This is really about personal preference! However, you might encounter them all.

results_pm_copd[["health_main"]]

Note: if the cursor is located within the square braces you can see all available options by pressing the tab key

results_pm_copd$health_main$impact_rounded

Note: after typing the $ sign you can see all available options by pressing the tab key and use the arrows & tab keys to select an option (or alternatively use the mouse)

Using the purrr::pluck function to select a list and then the dplyr::pull function extract values from a specified column

results_pm_copd |> purrr::pluck("health_main") |> dplyr::pull("impact_rounded")

Note: available options can’t be displayed automatically using these functions -> better suited for a more permanent analysis script

View(results_pm_copd) will “open” the variable in a new window within RStudio. Alternatively, you can go to the Environment tab and click on the variable to open the same view mode.

Example: attribute call with input uncertainties

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
  )

Let’s check the detailed output!

Tip

See the intro vignette for a detailed description of output columns (coming soon)

This is what the health_detailed output table looks like

results_pm_copd[["health_detailed"]][["raw"]] |> knitr::kable()
geo_id_disaggregated erf_ci exp_ci bhd_ci cutoff_ci pop_fraction impact prop_pop_exp rr_increment erf_shape exposure_name approach_risk health_outcome exposure_dimension exposure_type exp rr bhd cutoff pop_fraction_type rr_conc impact_rounded
1 central central central central 0.1138961 3501.9619 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.369 30747 5 paf 1.128536 3502
1 central central lower central 0.1138961 3189.0894 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.369 28000 5 paf 1.128536 3189
1 central central upper central 0.1138961 3644.6736 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.369 32000 5 paf 1.128536 3645
1 lower central central central 0.0440064 1353.0658 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.124 30747 5 paf 1.046032 1353
1 lower central lower central 0.0440064 1232.1801 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.124 28000 5 paf 1.046032 1232
1 lower central upper central 0.0440064 1408.2058 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.124 32000 5 paf 1.046032 1408
1 upper central central central 0.1780300 5473.8882 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.664 30747 5 paf 1.216589 5474
1 upper central lower central 0.1780300 4984.8398 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.664 28000 5 paf 1.216589 4985
1 upper central upper central 0.1780300 5696.9598 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.85 1.664 32000 5 paf 1.216589 5697
1 central lower central central 0.0899213 2764.8092 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.369 30747 5 paf 1.098806 2765
1 central lower lower central 0.0899213 2517.7955 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.369 28000 5 paf 1.098806 2518
1 central lower upper central 0.0899213 2877.4806 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.369 32000 5 paf 1.098806 2877
1 lower lower central central 0.0344604 1059.5528 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.124 30747 5 paf 1.035690 1060
1 lower lower lower central 0.0344604 964.8902 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.124 28000 5 paf 1.035690 965
1 lower lower upper central 0.0344604 1102.7316 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.124 32000 5 paf 1.035690 1103
1 upper lower central central 0.1416706 4355.9450 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.664 30747 5 paf 1.165054 4356
1 upper lower lower central 0.1416706 3966.7760 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.664 28000 5 paf 1.165054 3967
1 upper lower upper central 0.1416706 4533.4583 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 8.00 1.664 32000 5 paf 1.165054 4533
1 central upper central central 0.1453304 4468.4726 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.369 30747 5 paf 1.170043 4468
1 central upper lower central 0.1453304 4069.2501 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.369 28000 5 paf 1.170043 4069
1 central upper upper central 0.1453304 4650.5716 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.369 32000 5 paf 1.170043 4651
1 lower upper central central 0.0567717 1745.5580 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.124 30747 5 paf 1.060189 1746
1 lower upper lower central 0.0567717 1589.6063 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.124 28000 5 paf 1.060189 1590
1 lower upper upper central 0.0567717 1816.6929 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.124 32000 5 paf 1.060189 1817
1 upper upper central central 0.2247829 6911.4001 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.664 30747 5 paf 1.289961 6911
1 upper upper lower central 0.2247829 6293.9214 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.664 28000 5 paf 1.289961 6294
1 upper upper upper central 0.2247829 7193.0531 1 10 log_linear NA relative_risk same_input_output 1 population_weighted_mean 10.00 1.664 32000 5 paf 1.289961 7193